Decoding Airbnb location strategy for remote hosts: A location theory approach

Airbnb has transformed accommodation sharing and created new opportunities for hosts who manage properties remotely. Yet limited research has examined how remote hosts can strategically select locations to offset the disadvantages of operating without local presence. Drawing on location theory, this study develops a two-tier spatial evaluation framework that distinguishes between macro city-level and micro neighborhood-level drivers of listing performance. We assemble a longitudinal panel of 512,919 listings and 2.79 million quarterly observations across 30 diverse U.S. cities from March 2022 to March 2025. Using a two-way fixed effects model, we find that remotely managed listings receive approximately 12–16 % fewer quarterly reviews than locally managed listings. However, this gap narrows substantially when remote hosts invest in cities with stable tourism demand and steady economic growth. Performance further improves when listings are located in neighborhoods with strong walkability, proximity to cultural and recreational anchors, and moderate competitive density. Theoretically, the study extends location theory into platform-mediated lodging markets by conceptualizing location as a deliberate strategic decision rather than a fixed contextual attribute. Practically, the findings show that remote hosting performance depends not on physical proximity, but on alignment between spatial-economic environments and the operational capabilities of managing from a distance. These insights provide a location-based investment framework for hosts, data-driven decision guidance for platforms, and context-sensitive regulatory considerations for policymakers.